Forecasting Freeway On-Ramp Lane-Changing Behavior Based on GRU
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 147, Issue 12
Abstract
Lane changing is a fundamental driving task and is closely related to traffic operation. The safety performance of vehicle driving and traffic flow is supposed to be substantially improved if lane-changing behavior can be precisely predicted. To this end, a model based on the Gated Recurrent Unit (GRU) is proposed in this study for freeway on-ramp lane-changing behavior forecasting. One specific feature of the model is that it enables the filtering out of the lateral oscillation behavior and helps enhance forecast accuracy. The experiment results show that the model achieves an accuracy of 96.85% for lane-changing behavior forecasting, and outperforms the GRU model without lateral acceleration input and the LSTM model by 5.12% and 4.51%, respectively.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository or online in accordance with funder data retention policies (http://ops.fhwa.dot.gov/trafficanalysistools/ngsim.htm).
Acknowledgments
This research is partially supported by the National Natural Science Foundation of China under Grant No. 51775016 and No. L191002. The authors would also like to thank the insightful and constructive comments from anonymous reviewers.
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 147 • Issue 12 • December 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Jan 29, 2021
Accepted: Jul 20, 2021
Published online: Oct 6, 2021
Published in print: Dec 1, 2021
Discussion open until: Mar 6, 2022
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